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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Collabcrew - An intelligent Tool for Dynamic Task Allocation within a Software Development Team 11

Collabcrew - An Intelligent Tool for Dynamic Task Allocation...

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11th international conference on Software, Knowledge, Information Management and Applications (SKIMA)

作者： Samath, Shazna Udalagama, Dilantha Kurukulasooriya, Hansani Premarathne, Dilsha thelijjagoda, Samantha Sri Lanka Inst Informat Technol Dept Informat Technol Malabe Sri Lanka Sri Lanka Inst Informat Technol Dept Informat Syst Engn Malabe Sri Lanka

ISBN: (纸本)9781538646021

Currently in the IT industry, the people factor has become very critical when determining the quality of a software project. It is highly important that the correct person performs the relevant task and proper human resource allocation happens within the software project team to obtain successful outcome. this often needs critical thinking, regular team meetings and discussions. Typically a software project manager needs to be highly experienced with the team for this purpose and can be really complex and time consuming with the limited project schedules. this research work introduces a task management tool CollabCrew specially designed for the software development teams which dynamically allocate tasks based on the skills and previous work done by the team members. this uses historical data from its' own repository or from an external source to find useful information of the previous work done by the project team members to automatically allocate them for new tasks. this proposed system will be containing an Extract, Transform and Load (ETL) tool which will extract data from different data sources, a prediction model to predict the aptness of each team member for a given task and a peer review mining and summarization component to provide a viable way to extract features from peer reviews. then based on the result, the task allocation component will do the allocations in the most optimal and the feasible way for the project. Even though there are several commercially available task management tools, none has an intelligent component to automatically delegate work within the team. the scope of this work extends beyond the IT domain and a similar procedure can be adopted to develop a task allocation framework in other fields as well.

关键词： Software Project Management Task Allocation Machine learning data Mining Predictive Modelling Review Mining Extract Transform and Load (ETL) Business Intelligence Genetic Algorithm Sentimental Analysis

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Convolutional Neural Networks for Unsupervised Anomaly Detection in Text data 18th

Convolutional Neural Networks for Unsupervised Anomaly Detec...

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Gorokhov, Oleg Petrovskiy, Mikhail Mashechkin, Igor Lomonosov Moscow State Univ MSU Dept Comp Sci Moscow 119899 Russia

ISBN: (纸本)9783319689357;9783319689340

In this paper, we discuss the problem of anomaly detection in text data using convolutional neural network (CNN). Recently CNNs have become one of the most popular and powerful tools for various machine learning tasks. CNN's main advantage is an ability to extract complicated hidden features from high dimensional data with complex structure. Usually CNNs are applied in supervised learning mode. On the other hand, unsupervised anomaly detection is an important problem in many applications, including computer security, behavioral analytics, etc. Since there is no specified target in unsupervised mode, traditional CNN's objective functions cannot be used. In this paper, we develop a specific CNN architecture. It consists of one convolutional layer and one subsampling layer, we use RBF activation function and logarithmic loss function on the final layer. Minimization of the corresponding objective function helps us to calculate the location parameter of the features' weights discovered on the last network layer. We use l(2)-regularization to avoid degenerate solution. Proposed CNN has been tested on anomalies discovering in a stream of text documents modeled with well-known Enron dataset, where proposed method demonstrates better results in comparison with the traditional outlier detection methods based on one-class SVM and NMF.

关键词： Anomaly detection Text mining Convolutional neural network One-class classification Regularization SVM NMF

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Robust classification of crisis-related data on social networks using convolutional neural networks 11

Robust classification of crisis-related data on social netwo...

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11th international conference on Web and Social Media, ICWSM 2017

作者： Nguyen, Dat Tien Al Mannai, Kamela Ali Joty, Shafiq Sajjad, Hassan Imran, Muhammad Mitra, Prasenjit Qatar Computing Research Institute HBKU Doha Qatar Pennsylvania State University University Park United States

ISBN: (纸本)9781577357889

the role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning. the scarcity of labeled data, particularly in the early hours of a crisis, delays the learning process. Existing classification methods require a significant amount of labeled data specific to a particular event for training plus a lot of feature engineering to achieve best results. In this work, we introduce neural network based classification methods for identifying useful tweets during a crisis situation. At the onset of a disaster when no labeled data is available, our proposed method makes the best use of the out-of-event data and achieves good results. © Copyright 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Disasters

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Interactive data Visualization Using Dimensionality Reduction and Dissimilarity-Based Representations 18th

Interactive Data Visualization Using Dimensionality Reductio...

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Pena-Unigarro, D. F. Rosero-Montalvo, P. Revelo-Fuelagan, E. J. Castro-Silva, J. A. Alvarado-Perez, J. C. theron, R. Ortega-Bustamante, C. M. Peluffo-Ordonez, D. H. Univ Narino Pasto Colombia Univ Tecn Norte Ibarra Ecuador Inst Tecnol Super 17 Julio Ibarra Ecuador Univ Surcolombiana Neiva Colombia Coorporac Univ Autonoma Narino Pasto Colombia Univ Salamanca Salamanca Spain

ISBN: (纸本)9783319689357;9783319689340

this work describes a new model for interactive data visualization followed from a dimensionality-reduction (DR)-based approach. Particularly, the mixture of the resulting spaces of DR methods is considered, which is carried out by a weighted sum. For the sake of user interaction, corresponding weighting factors are given via an intuitive color-based interface. Also, to depict the DR outcomes while showing information about the input high-dimensional data space, the low-dimensional representations reached by the mixture is conveyed using scatter plots enhanced with an interactive data-driven visualization. In this connection, a constrained dissimilarity approach define the graph to be drawn on the scatter plot.

关键词： data visualization Dimensionality reduction Pairwise dissimilarity

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Semi-supervised Regularized Discriminant Analysis for EEG-Based BCI System 18th

Semi-supervised Regularized Discriminant Analysis for EEG-Ba...

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Xin, Yuhang Wu, Qiang Zhao, Qibin Wu, Qi Shandong Univ Sch Informat Sci & Engn Jinan 250010 Shandong Peoples R China RIKEN Ctr Adv Intelligence Project Tensor Learning Unit Chuo Ku Tokyo 1030027 Japan Shandong Univ Qilu Hosp Jinan 250012 Shandong Peoples R China

ISBN: (纸本)9783319689357;9783319689340

Brain-Computer interface (BCI) is a new technique which allows direction connection between human and computer or other external device. It employs the classification of event-related potential to control the equipment rather than using language or limb movement. Currently, one of the most important issues for ERP-based BCIs is that the ERP classification performance degrades when the training number of samples is small. In order to solve this problem, semi-supervised regularized discriminant analysis (SRDA) was proposed to extract features and classify ERP patterns by integrating semi-supervised learning and regularization approach. the labeled data was used to maximize the separability between different classes and calculate the within-class covariance matrix by regularization. the labeled and unlabeled data were employed to construct the penalty term by neighbor graph. Our proposed approach was evaluated on the BCI Competition Challenge dataset and the simulation results indicated that it achieved a better accuracy than the traditional algorithms.

关键词： Brain computer interface

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Machine learning or discrete choice models for car ownership demand estimation and prediction? 5

Machine learning or discrete choice models for car ownership...

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5th IEEE international conference on Models and Technologies for intelligent Transportation Systems, MT-ITS 2017

作者： Paredes, Miguel Hemberg, Erik O'Reilly, Una-May Zegras, Chris CSAIL and DUSP Massachusetts Institute of Technology CambridgeMA02139 United States

ISBN: (纸本)9781509064847

Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. they show how some distinct choice of human behavior or preference influences a decision. they are also used to project future demand estimates to support policy exploration. this latter use for prediction is indirectly aligned with and conditional to the model's estimation which aims to fit the observed data. In contrast, machine learning models are derived to maximize prediction accuracy through mechanisms such as out-of-sample validation, non-linear structure, and automated covariate selection, albeit at the expense of interpretability and sound behavioral theory. We investigate how machine learning models can outperform discrete choice models for prediction of car ownership using transportation household survey data from Singapore. We compare our household car ownership model (multinomial logit model) against various machine learning models (e.g. Random Forest, Support Vector Machines) by using 2008 data to derive, i.e. estimate models that we then use to predict 2012 ownership. the machine learning models are inferior to the discrete choice model when using discrete choice features. However, after engineering features more appropriate for machine learning they are superior. these results highlight both the cost of applying machine learning models in econometric contexts and an opportunity for improved prediction and better urban policy making through machine learning models with appropriate features. © 2017 IEEE.

关键词： Forecasting

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Perceptions and preferences of high school students in STEM: A case study in Connecticut and Mississippi 11

Perceptions and preferences of high school students in STEM:...

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11th international Multi-conference on Society, Cybernetics and Informatics, IMSCI 2017

作者： Zhou, Bin Anderson, Clifford Wang, Feng Li, Lin Department of Engineering Central Connecticut State University New BritainCT06050 United States Department of Civil and Environmental Engineering Jackson State University JacksonMI39217 United States

ISBN: (纸本)9781941763650

this study uses data collected at two National Summer Transportation Institute (NSTI) programs in Connecticut and Mississippi to investigate high school students' perceptions and preferences about education in science, technology, engineering and mathematics (STEM). Family background has a significant impact on a high school student's interest in STEM, as shown during the student recruitment stage and by the analysis of the students' college education plans prepared upon graduation from the two NSTI programs. the building exercise and competition instrument is the most effective among the few examined, while passive learning is not what young people prefer when briefly introduced in the two NSTI programs.

关键词： Students

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WENC: HTTPS Encrypted Traffic Classification Using Weighted Ensemble learning and Markov Chain 16

WENC: HTTPS Encrypted Traffic Classification Using Weighted ...

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16th IEEE international conference on Trust, Security and Privacy in Computing and Communications / the 11th IEEE international conference on Big data Science and engineering / the 14th IEEE international conference on Embedded Software and Systems

作者： Pan, Wubin Cheng, Gaung Tang, Yongning Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China Minist Educ Key Lab Comp Network & Informat Integrat Beijing Peoples R China Illinois State Univ Sch Informat Technol Normal IL 61761 USA

ISBN: (纸本)9781509049066

SSL/TLS protocol is widely used for secure web applications (i.e., HTTPS). Classifying encrypted SSL/TLS based applications is an important but challenging task for network management. Traditional traffic classification methods are incapable of accomplishing this task. Several recently proposed approaches that focused on discriminating defining fingerprints among various SSL/TLS applications have also shown various limitations. In this paper, we design a Weighted ENsemble Classifier (WENC) to tackle these limitations. WENC studies the characteristics of various sub-flows during the HTTPS handshake process and the following data transmission period. To increase the fingerprint recognizability, we propose to establish a second order Markov chain model with a fingerprint variable jointly considering the packet length and the message type during the process of HTTPS handshake. Furthermore, the series of the packet lengths of application data is modeled as HMM with optimal emission probability. Finally, a weighted ensemble strategy is devised to accommodate the advantages of several approaches as a unified one. Experimental results show that the classification accuracy of the proposed method reaches 90%, with an 11% improvement on average comparing to the state-of-the-art methods.

关键词： SSL/TLS HTTPS services HMM Markov chain, weighted ensemble

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Proceedings - 2017 5th international conference on Applied Computing and Information Technology, 2017 4th international conference on Computational Science/Intelligence and Applied Informatics and 2017 1st international conference on Big data, Cloud Computing, data Science and engineering, ACIT-CSII-BCD 2017

Proceedings - 2017 5th International Conference on Applied C...

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5th international conference on Applied Computing and Information Technology, 2017 4th international conference on Computational Science/Intelligence and Applied Informatics and 2017 1st international conference on Big data, Cloud Computing, data Science and engineering, ACIT-CSII-BCD 2017

ISBN: (纸本)9781538633021

the proceedings contain 44 papers. the topics discussed include: a learning material for physics experiment with high-accuracy using computer vision technique;automatic classification of remarks in werewolf BBS;a rapid incremental frequent pattern mining algorithm for uncertain data;global and local bursts detection in streaming data;two-mode three-way dominance points model for periodic dissimilarity;an intelligent noninvasive taste detection app for watermelons;automated risk identification of CMMI project planning using ontology;and depth recognition in 3D translucent stereoscopic imaging of medical volumes by means of a glasses-free 3d display.

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Optimal design of reversible parity preserving new Full adder / Full subtractor 11

Optimal design of reversible parity preserving new Full adde...

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2017 11th international conference on intelligent Systems and Control, ISCO 2017

作者： Kumar, P.Kiran Rao, P.Prasad Kakarla, Hari Kishore Dept. of ECE KL University India Dept. of ECE Vaagdevi Engineering College India

ISBN: (纸本)9781509027170

the widely using CMOS technology implementing with irreversible logic will hit a scaling limit beyond 2020 and the major limiting factor is increased power dissipation. the irreversible logic is replaced by reversible logic to decrease the power dissipation. the devices implemented with reversible logic gates will have demand for the upcoming future computing technologies as they consumes less power. Reversible logic has applications in Low Power VLSI, Quantum Computing, Nanotechnology and Optical computing. this paper proposes the design of a optimal fault tolerant Full adder / Full subtractor. For this logic circuit input parity and output parity is same hence it is called parity preserving circuit. the proposed method require less complexity, less hardware, minimum number of gates, minimum number of garbage inputs and minimum number of constant inputs than existing methods. © 2017 IEEE.

关键词： Optical data processing

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